Search Results for author:
Found 11 papers, 4 papers with code
On Semi-parametric Inference for BART
There has been a growing realization of the potential of Bayesian machine learning as a platform that can provide both flexible modeling, accurate predictions as well as coherent uncertainty statements.
Tree Bandits for Generative Bayes
Finally, we successfully apply our approach to the problem of masked image classification using deep generative models.
Deep Bayes Factors
After training, our deep learning approach enables rapid evaluations of the Bayes factor estimator at any fictional data arriving from either hypothesized model, not just the observed data $Y_0$.
Sparse Bayesian Multidimensional Item Response Theory
In our simulation study, we show that our method reliably recovers both the factor dimensionality as well as the latent structure on high-dimensional synthetic data even for small samples.
On Mixing Rates for Bayesian CART
We show polynomial mixing of Twiggy Bayesian CART without assuming that the signal is connected on a tree.
Variable Selection via Thompson Sampling
Thompson sampling is a heuristic algorithm for the multi-armed bandit problem which has a long tradition in machine learning.
BIG-bench Machine Learning
Interpretable Machine Learning
+3
Adaptive Bayesian SLOPE -- High-dimensional Model Selection with Missing Values
We consider the problem of variable selection in high-dimensional settings with missing observations among the covariates.
Methodology Applications Computation
On Theory for BART
Laying the foundations for the theoretical analysis of Bayesian forests, Rockova and van der Pas (2017) showed optimal posterior concentration under conditionally uniform tree priors.
Posterior Concentration for Sparse Deep Learning
As an aside, we show that SS-DL does not overfit in the sense that the posterior concentrates on smaller networks with fewer (up to the optimal number of) nodes and links.
Variance prior forms for high-dimensional Bayesian variable selection
In a similar way, we show that conjugate priors for linear regression, which induce prior dependence, can lead to such underestimation in the Bayesian high-dimensional regression setting.
Methodology
Simultaneous Variable and Covariance Selection with the Multivariate Spike-and-Slab Lasso
We propose a Bayesian procedure for simultaneous variable and covariance selection using continuous spike-and-slab priors in multivariate linear regression models where q possibly correlated responses are regressed onto p predictors.
Methodology
